2025-06-10
Assistant Professor at Yale School of Public Health
Teach statistical modeling and study design
Research focus on infectious disease study design and cluster-randomized trials
Allows use of routinely-collected data
Evaluates interventions in-context
Provides “real world evidence”/population impact
Answers questions randomized trials and observational studies cannot
But … has threats to internal and external validity
8:30–9:00 Introduction and core DID issues
9:00–9:45 Advanced DID and staggered adoption
9:45–10:30 Analysis 1: Advanced DID of COVID-19 vaccine mandates
10:40–11:15 Introduction to synthetic control
11:15–11:45 Analysis 3: SC of Ohio’s COVID-19 vaccine lottery
11:45–12:15 Advanced SC methods overview
12:15–12:30 Analysis 4: Advanced SC of multiple states’ COVID-19 vaccine lotteries
Understand, interpret, and critique the use of DID and SC in epidemiology
Gain familiarity with state-of-the-art methods related to DID and SC and identify resources for further exploration
Contextualize the assumptions needed for causal inference from quasi-experiments
Implement staggered adoption DID and SC analyses and diagnostics/inference in R
I will focus here on infectious disease examples from published literature with available data. Some issues are specific to ID, while others are not, but they illustrate the points of how to approach these questions.
All materials: https://github.com/leekshaffer/Epi-QEs/
Parallel trends (in expectation of potential outcomes)
No spillover
No anticipation/clear time point for treatment
Re-scale the outcome
Incorporate covariates
Include more or fewer units and/or time periods
Estimand Interpretation
DID estimates the Average Treatment Effect on the Treated (ATT).
This may not be generalizable to other units, including the untreated units in the study.
Internal validity may be high if the assumptions are justified.
External validity may be low because of limited transportability of the ATT and limited information on effect heterogeneity.
Incorporating additional units/periods can reduce variance, but may also risk violating the assumptions
Generally conducted with limited, carefully-selected units: low bias but high variance
Examples
More distant vs. closer untreated units
Incorporating more untreated units
Incorporating more recent time periods
Advantages:
Simple to implement
Uses summary data
No need to model time trends or collect covariates
Straightforward interpretation
Disadvantages/Limitations:
Targets ATT not ATE
Need to justify key assumptions
Requires careful selection of controls
Limited inference with few units/periods